What if the biggest obstacle to industrial AI isn't the technology itself, but the data businesses are trying to use with it? And how much could manufacturers and industrial operators achieve if they could connect information from across the entire life of their assets?
Recording live at IFS Unleashed 2026 in Orlando, I speak with Shawn Gregerson, VP APM & MES Business Development at AVEVA, about how industrial companies are using AI, operational data, and predictive maintenance to improve reliability and make more informed business decisions.
Shawn explains why industrial AI is increasingly becoming a data challenge rather than an algorithmic one. Although AI models and analytical techniques have developed significantly, many organizations still struggle with disconnected information spread across engineering, operations, maintenance, and asset management systems.
The conversation examines how AVEVA is bringing these different sources together through its industrial knowledge graph technology, creating a more reliable foundation for AI applications that need to understand the relationships between assets, processes, and operational conditions.
They also discuss the growing financial pressures facing industrial organizations. Shawn highlights the increasing cost of downtime and the challenge of maintaining assets that are operating beyond their original design life.
With AVEVA and IFS working together on Continuous Asset Decision Intelligence, or CADi, the discussion turns to connecting asset planning, design, operations, maintenance, and optimization so that decisions can be informed by real operating conditions.
Shawn also shares where industrial AI is already delivering results, including predictive maintenance systems that monitor equipment health, identify early warning signs, and help maintenance teams determine what action to take before failures occur. Other applications include predictive quality, manufacturing throughput, and the use of generative AI to make industrial information easier to access.
Looking beyond individual applications, they consider how connected industrial data could support agentic workflows and enable businesses to automate more of the tasks that employees currently perform manually.
As industrial organizations face pressure to improve efficiency while extending the life of existing equipment, could connecting their operational data deliver greater value than investing in increasingly sophisticated AI models alone?
Listen to the conversation and share your thoughts on how industrial AI is changing maintenance, asset management, and operational decision-making.
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[00:00:27] Welcome back to the show. Today I'm recording this episode at the IFS Unleashed 2026 event in Orlando. Well, I've been hearing plenty about the potential of industrial AI. But one question keeps coming up. How can organisations make better decisions when the information they need is scattered across so many different systems? Think about factories producing our food.
[00:01:00] The infrastructure delivering our clean water. Or the equipment keeping essential services running. Much of that machinery is ageing. And the cost of unexpected downtime, well, that continues to climb too. So my guest today is Sean Gregerson. He's from a company called Aviva, where he leads the company's asset performance management and manufacturing execution systems businesses.
[00:01:28] And today we're going to talk about why AI success often begins with getting industrial data into shape and how predictive maintenance is actually evolving. And what Aviva's partnership with IFS could mean for managing assets throughout their working lives. And we'll also explore how companies can turn all that operational intelligence into better maintenance decisions, smarter investments and measurable business results.
[00:01:56] Yeah, no hype. We're just going to focus on results today. And with that scene perfectly set, let me beam your ears directly to the show floor where you can join the conversation with myself and Sean now. So here we are recording at IFS Unleashed in Orlando. And for everybody listening and watching, can you tell everyone a little about who you are and what you do? Yeah, sure. So I'm Sean Gregerson. I'm responsible for Aviva's asset performance management and MES software businesses.
[00:02:26] And for anyone that doesn't know what Aviva does, Aviva is a global leader in industrial software. And our technologies are helping the industrial leaders deliver things like clean water, food, medicine and pharmaceuticals and infrastructure.
[00:02:48] And so this is what the Aviva business is all about, is providing that technology that's helping our customers deliver consistently in high quality life's essentials. And my role is primarily focused around helping our customers to operate their assets at higher levels, performance, reliability and from an overall manufacturing optimization perspective.
[00:03:15] And I'm curious, from what you're seeing here, how are industrial organizations, how are they actually using AI and operational data to move from just reactive to that utopia of predictive maintenance? Yeah, there's a number of ways that they're using the data that we have for AI today. What I would say is AI, and many are starting to learn this, is more of a data problem than it is an algorithmic problem or model problem today.
[00:03:40] The models are there, the AI algorithmic books are written, but you have to have clean, structured, governed data in order to be able to scale AI within the business. So that's where we start often with our customers is fusing together all of the engineering design data with the operations data, with the asset management data, with the optimization system data and maintenance data.
[00:04:05] So that we can bring this into what we call our intelligence industrial knowledge graph technology. And what it's doing is fusing together all those domains and dimensions of the industrial data across that entire asset lifecycle. And then that provides the foundation, the governed data foundation, to be able to scale and proceed with AI at scale and at pace.
[00:04:29] Because otherwise, every AI project becomes a data integration project before you even get to the AI output. And how are Aviva and IFS? How are you connecting industrial intelligence with enterprise execution to turn data into faster, more informed operational decisions? Tell me a little bit more about that and that relationship. Yeah.
[00:04:49] So if we look back over the last decades, there's been a lot of investment in these point level type solutions to do operational technologies, maintenance technologies, maintenance execution technologies, capital planning software solutions. And it's clear those investments are paying off and they have a fundamental return. Those investments will continue as we go forward.
[00:05:16] But what we're seeing is that over the last five years, the cost of downtime has increased in industry by 60%. And we have to look no further than the reality that 40% of our industrial assets are operating beyond their intended life and designed life. And so what that tells us is that we've got to go beyond what we're doing today.
[00:05:42] And what we believe that the answer to that is, is what we call CADDI, continuous asset decision intelligence. And this is where we are closing this decision loop continuously across the capital planning, design, build, operate, maintain, and optimize asset lifecycle and value chain. And that's what we're doing together with IFS that's new and revolutionary for the industry.
[00:06:08] And over the last few years, there's been a big focus on ROI after a few question marks over the value that AI can bring. Thankfully, all that is changing now. So where are you seeing industrial AI really delivering measurable value? Yeah, look, at Aviva, we've been doing AI for over 20 years now. So this isn't new to us. And some of the software applications we built 20 years ago, we've continued to iterate, update, latest, greatest technology.
[00:06:37] They're still in operation and part of our active software portfolio today. The places where you see AI clearly demonstrating success in the industry today are things like predictive maintenance, where you're actually running these predictive models that are looking at the health of each critical asset, compressors, motors, turbines, generators, these types of assets.
[00:07:02] And what it's doing is learning from the historical data that's available for that asset, unique signature for each one of these assets. And it's able to then monitor the health of that asset real time, detect these issues, move from there to prescriptive maintenance and prognostics around time to failure.
[00:07:23] So being able to leverage AI to go from early warning detection to prescriptive guidance on what action should be taken to how much time do I have to remediate this issue before a failure is likely to occur. And so those are core use cases. You see use cases of AI around things like predictive quality, predictive production throughput and manufacturing.
[00:07:45] And now what we're seeing is these other use cases where once we take and fuse together all the domains and dimensions of the industrial data across the entire industrial landscape, we can then provide searchability of this data through traditional Google type search capabilities, but now also this generative AI based search capabilities.
[00:08:07] And what that does is allows us to give our customers a 360 degree perspective of the entire asset across all of its life. And that is another really key use case of this. And then once you do that, the next step obviously is agentic workflows to start to drive through AI agents automating a lot of the tasks that people are manually performing today. And you've mentioned a few use cases.
[00:08:35] And if we look at what's happening on the shop floor right now, how are you seeing trusted connected data helping organization scale beyond isolated pilots into those day-to-day operations? What are you seeing there? What's really happening? Yeah. Yeah. So there's still a fair bit of work to do on the data infrastructure side. And we've seen that in many cases, maybe these data lakes have been put in place, but the data is not structured. It's not contextualized. It's not usable.
[00:09:04] It's not AI ready. And so that's what we do is we bring all this data together, fuse it together in a way that it's shaped properly so that these AI capabilities could be fully utilized. And how are you seeing asset lifecycle management helping organizations maybe sharpen their maintenance strategies to make more informed capital investment decisions right across the operations? Anything you can share around that too? Yeah. I mean, absolutely.
[00:09:33] We've got to get better in our capital decision making. And largely today, many of the capital decisions are made on just historical profiling or just guesswork and not on operational reality. So that has to change. And that's where this data infrastructure closing the loop to be able to bring all these aspects of the assets life to bear when we're making these asset decisions makes all the difference.
[00:10:03] And for everybody listening and watching that can't attend the event, you're on stage. There's a booth here as well. And you're a sponsor. What are you talking about on stage today? Yeah, that's right. So we're speaking here at the conference. We've got a booth set up showing off all of our latest, greatest technology.
[00:10:23] And at the conference, we're talking and speaking about our partnership with IFS and this new capability that we're developing that we call CADDI, this continuous asset decision intelligence. Awesome. Well, I know you've literally got 12 minutes before you're on stage. So a big thank you for joining me today. Thank you so much. So a big thank you to Sean there for joining me at IFS Unleash in Orlando, especially with literally just minutes before he was heading on stage.
[00:10:53] And one of the things that really stood out to me from our conversation is Sean's reminder that AI is often more of a data challenge than a technology challenge. And organizations I've spent years collecting enormous amounts of information. But unless that data is connected, structured and understood in context, even the most sophisticated AI models will struggle to deliver those meaningful results.
[00:11:20] And I also found the discussion around continuous asset decision intelligence particularly interesting here. Because connecting everything from engineering design and capital investment to maintenance and everyday operations could all help businesses make better decisions throughout an asset's entire life.
[00:11:40] And with so much industrial equipment operating beyond its intended lifespan, I think those decisions are becoming increasingly important. And I will be sharing more conversations from Orlando. So please keep following Tech Talks Daily wherever you listen. And remember, Tech Talks Network, we've got a whole heap of other podcasts over there too. So thanks as always for listening and I'll speak with you all again soon. Bye for now.
[00:12:14] Bye for now.